From Human Child to Grey Parrot: Exploring a Common Model of Word Meaning Extension Across Species
Bibliographic record
Abstract
Word meaning extension refers to the process by which a single word form develops multiple related meanings. Prior studies demonstrate that meaning extension at diverse timescales, from decades-long historical change and to month-long changes in child overextension, is accounted for by models grounded in conceptual relations across knowledge types. Whether this framework generalizes to other species remains an open question. We address this question with a probabilistic model of overextension based on various knowledge types to predict word choice of nonhuman animals. As a starting point, we compared cases of overextension from Apollo - a grey parrot who has acquired some English words - to the cases of overextension documented in child language acquisition. We apply an established model of child overextension to a novel parrot dataset of over 200 referent-utterance pairs (e.g., bead-"ball") collected from Apollo's YouTube channel and test whether the child model can predict parrot word choice. Our results show that Apollo's overextension can be predicted by the multimodal model of child overextension better than baseline models that rely on frequency or sound similarity. We also find independent evidence supporting the role of different knowledge types from Alex, a grey parrot, who features prominently in prior research on animal acquisition of human language. Our findings suggest that a common model might account for the cognitive ability of word overextension identifiable in a species that diverged from humans about 320 million years ago. We discuss potential limitations and future research directions that may further strengthen the current findings.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".